Scaling Big Data Applications in Smart City with Coresets

D 2019

Scaling Big Data Applications in Smart City with Coresets

TRANG, Le Hong; Hind BANGUI; Mouzhi GE and Barbora BÜHNOVÁ

Basic information

Original name

Scaling Big Data Applications in Smart City with Coresets

Authors

TRANG, Le Hong; Hind BANGUI (504 Morocco, belonging to the institution); Mouzhi GE (156 China, belonging to the institution) and Barbora BÜHNOVÁ (203 Czech Republic, guarantor, belonging to the institution)

Edition

Prague, Czech Republic, Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1, p. 357-363, 7 pp. 2019

Publisher

SciTePress

Other information

Language

English

Type of outcome

Proceedings paper

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

Germany

Confidentiality degree

is not subject to a state or trade secret

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/00216224:14610/19:00109826

Organization unit

Institute of Computer Science

ISBN

978-989-758-377-3

DOI

http://dx.doi.org/10.5220/0007958803570363

UT WoS

000570730200042

EID Scopus

2-s2.0-85072973638

Keywords in English

Big Data; Classification; Coreset; Clustering; Sampling; Smart City

Abstract

In the original language

With the development of Big Data applications in Smart Cities, various Big Data applications are proposed within the domain. These are however hard to test and prototype, since such prototyping requires big computing resources. In order to save the effort in building Big Data prototypes for Smart Cities, this paper proposes an enhanced sampling technique to obtain a coreset from Big Data while keeping the features of the Big Data, such as clustering structure and distribution density. In the proposed sampling method, for a given dataset and an e > 0, the method computes an e-coreset of the dataset. The e-coreset is then modified to obtain a sample set while ensuring the separation and balance in the set. Furthermore, by considering the representativeness of each sample point, our method can helps to remove noises and outliers. We believe that the coreset-based technique can be used to efficiently prototype and evaluate Big Data applications in the Smart City.

Links

EF16_013/0001802, research and development project

Name: CERIT Scientific Cloud

Cite

TRANG, Le Hong; Hind BANGUI; Mouzhi GE and Barbora BÜHNOVÁ. Scaling Big Data Applications in Smart City with Coresets. Online. In Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1. Prague, Czech Republic: SciTePress, 2019, p. 357-363. ISBN 978-989-758-377-3. Available from: https://dx.doi.org/10.5220/0007958803570363.

@inproceedings{1536678,
   author = {Trang, Le Hong and Bangui, Hind and Ge, Mouzhi and Bühnová, Barbora},
   address = {Prague, Czech Republic},
   booktitle = {Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1},
   doi = {http://dx.doi.org/10.5220/0007958803570363},
   keywords = {Big Data; Classification; Coreset; Clustering; Sampling; Smart City},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Prague, Czech Republic},
   isbn = {978-989-758-377-3},
   pages = {357-363},
   publisher = {SciTePress},
   title = {Scaling Big Data Applications in Smart City with Coresets},
   url = {http://dx.doi.org/10.5220/0007958803570363},
   year = {2019}
}

TY  - CONF
ID  - 1536678
AU  - Trang, Le Hong - Bangui, Hind - Ge, Mouzhi - Bühnová, Barbora
PY  - 2019
TI  - Scaling Big Data Applications in Smart City with Coresets
PB  - SciTePress
CY  - Prague, Czech Republic
SN  - 9789897583773
KW  - Big Data
KW  - Classification
KW  - Coreset
KW  - Clustering
KW  - Sampling
KW  - Smart City
UR  - http://dx.doi.org/10.5220/0007958803570363
L2  - http://dx.doi.org/10.5220/0007958803570363
N2  - With the development of Big Data applications in Smart Cities, various Big Data applications are proposed within the domain. These are however hard to test and prototype, since such prototyping requires big computing resources. In order to save the effort in building Big Data prototypes for Smart Cities, this paper proposes an enhanced sampling technique to obtain a coreset from Big Data while keeping the features of the Big Data, such as clustering structure and distribution density. In the proposed sampling method, for a given dataset and an e > 0, the method computes an e-coreset of the dataset. The e-coreset is then modified to obtain a sample set while ensuring the separation and balance in the set. Furthermore, by considering the representativeness of each sample point, our method can helps to remove noises and outliers. We believe that the coreset-based technique can be used to efficiently prototype and evaluate Big Data applications in the Smart City.
ER  -

TRANG, Le Hong; Hind BANGUI; Mouzhi GE and Barbora BÜHNOVÁ. Scaling Big Data Applications in Smart City with Coresets. Online. In \textit{Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1}. Prague, Czech Republic: SciTePress, 2019, p.~357-363. ISBN~978-989-758-377-3. Available from: https://dx.doi.org/10.5220/0007958803570363.

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